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Volume 45 Issue 3
May  2021
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Difference test of paper ashes by XRF combined with multivariate statistical analysis

  • Received Date: 2020-02-14
    Accepted Date: 2020-06-18
  • In order to quickly and accurately test and identify the residual paper ashes on the scene of the case, 32 paper ashes from different sources and uses were determined by X-ray fluorescence spectrometry combined with system clustering and principal component analysis of multivariate statistics. Theoretical analysis and experimental verification were then carried out. The results show that the samples can be distinguished accurately according to the types and contents of the elements in the samples. At the same time, 32 samples are divided into five categories, and the classification results of the two methods are basically the same. The cumulative variance contribution rate of PC2 is as high as 99.86%. This research method does not destroy the sample, has good reproducibility, and the results are scientific and ideal, which provides a theoretical basis for the examination of trace evidence.
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Difference test of paper ashes by XRF combined with multivariate statistical analysis

  • Institute of Criminal Investigation, People's Public Security University of China, Beijing 100038, China

Abstract: In order to quickly and accurately test and identify the residual paper ashes on the scene of the case, 32 paper ashes from different sources and uses were determined by X-ray fluorescence spectrometry combined with system clustering and principal component analysis of multivariate statistics. Theoretical analysis and experimental verification were then carried out. The results show that the samples can be distinguished accurately according to the types and contents of the elements in the samples. At the same time, 32 samples are divided into five categories, and the classification results of the two methods are basically the same. The cumulative variance contribution rate of PC2 is as high as 99.86%. This research method does not destroy the sample, has good reproducibility, and the results are scientific and ideal, which provides a theoretical basis for the examination of trace evidence.

引言
  • 对犯罪现场遗留的纸张及其灰烬的分析检验是法庭科学研究的重要课题之一。纸张灰烬物证常见于各类案件现场,犯罪嫌疑人常用此手段隐藏犯罪信息[1]。在公安实践应用中,系统科学地分析鉴别案件现场残留的各种纸张灰烬,对于火灾原因的认定、提供案件侦破线索等有重要意义[2]。因此,对纸张灰烬的鉴别检验,有益于侦查人员确定下一步侦查方向和范围,为案件分析提供思路。目前,对纸张灰烬的检验方法主要有扫描电镜/能谱法[2-4]等。X射线荧光光谱法(X-ray fluorescence spectrometer,XRF)因其灵敏度高、分析试样不受破坏、能实现多元素同时测定等优点广泛应用于物证鉴定[5-7]。多元统计学是化学计量常用方法,包括回归分析、聚类分析、主成分分析等,借助多元统计分析,可实现数据降维、分组以及分类,挖掘多变量的数据之间的关系,实现对样本的有效归类[8-10]

    本实验中应用X射线荧光光谱,对32个不同来源和种类的纸张灰烬样品中的无机元素进行定性和半定量分析,以获取原纸张所含无机元素的种类和含量,并借助统计产品与服务解决方案(statistical product and service solutions, SPSS)软件,采用系统聚类分析和主成分分析对实验数据进行处理和验证,从多变量的数据中提取信息和规律,得到了较为满意的结果。

1.   实验
  • X-MET8000X射线荧光光谱仪(牛津仪器),Rh靶,电压为45kV,电流为40μA,功率为1.8kW,测试时间为80s。

  • 不同来源和种类的纸张灰烬样品共32个(部分样品见表 1)。

    sample
    number
    typesourcespecification
    (cm×cm)
    1napkinVinda13.5×19.5
    2worksheetYurongfeng notebook20×14
    4worksheetPPSUC scratch paper26×18
    6shoebox paperBASTO
    8xuan papercopybook
    9copy papercopybook
    10postcardPPSUC postcard15×10
    12cardboardLianhua Qingwen capsule box20×14
    13cardboardSITILON facial mask box32×16
    14cardboardFranzzi cookie box25×19
    17plastic tissueVia lemon tea20×12
    18cardboardDHC lipstick packaging box18×6
    19cardboardMac lipstick packaging box8×6
    20cardboardZhizhu sock box16×6
    21cardboardauberge bracelet box22×8
    22newspaperPPSUC souvenir20×14
    25description paperauberge description25×8
    26thermal paperPPSUC supermarket invoice22×5
    27cardboardfruit knife packing box18×5
    28business cardDoraemon’s pocket9×5
    29express detailsZTO express10×10
    30oil surface stickerstickers10×8
    31oil paperpin-up picture22×18
    32corrugated paperexpress box

    Table 1.  Partial sample table of paper ash samples

  • 取一定量样品置于干锅中点燃,完全燃烧后,待自然冷却后,将灰烬样品混合均匀,移至洁净样品塑料包装袋上,待测。在上述实验条件下,分别对32个样品依次进行测定,每个样品测3次取平均值。

2.   结果与讨论
  • 不同来源和种类的纸张灰烬样品由于原料和加工工艺的不同,所含元素种类和含量会有所区别。测定结果如表 2所示。

    sample
    number
    CaClFeZnTiPbBr
    1305457126234690711191
    2260417224151840420
    37070124137540513572
    41937414692441634223310
    586180189137553993321
    6435119175219782772972
    710201531853904824
    81983571671875530631799
    9338688171372092000
    1078210415511007049338
    11340016020904087812
    12205386126111080460
    1346047618311533166131
    1470744915413097047170
    152700146115790690
    1628301332128413591493
    1728211081151232502300
    1815871901521081244270
    1992206141223451148941
    20102024691942296115460
    21256354190136470400
    22295123135227270611014
    25360414164189491300
    2640446912920601067469
    274203541982132115791023
    2831953421924399058401
    296151231721730714450
    3031644014229952000
    3168895201132370330
    328340142725259000

    Table 2.  Major element analysis results of paper ash samples(μg/g)

    根据表 2中的实验数据,对样品进行定性和半定量分析。Ca元素来源于常见填料CaCO3[11];Zn元素来源于造纸常用漂白剂如硫酸锌[12];Cl元素来源于造纸工业中含氯漂白剂,如氯、次氯酸盐和二氧化氯;Br元素来源于用于纸张杀菌防霉的杀菌剂和抑菌剂[13];Ti元素来源于纸张填料中的二氧化钛,二氧化钛广泛用于增加纸张的白度和光泽度[14];Pb元素可能来源于纸张的印刷过程[15];Fe元素来源于造纸中使用的复盐、植物的吸收和加工设备等[16];Sn元素来源于纸张中的防腐剂和阻燃剂。根据元素的组成可以将样品进行分为9组,如图 1所示。图中“+”表示含有该元素, “-”表示不含该元素。

    Figure 1.  Grouping results of paper ash samples

    对同一组中的样品,可根据元素含量的比值进行区分,以含Cl,Br,Pb,Ti这一组的5个样品为例,根据样品中Zn/Ca和Br/Cl的比值不同,可以达到区分目的,结果见表 3

    sample numberZn/CaBr/Cl
    650.525.55
    894.725.03
    1325.070.07
    19242.884.57
    2750.762.89

    Table 3.  Results of the same group of sample

    根据这组样品中Zn/Ca和Br/Cl的比值,可以达到区分目的。

  • 运用统计学的分析方法将实验数据与体系状态建立联系是微量物证研究常用的手段。系统聚类又称为凝聚性层次聚类,主要是根据数据之间的距离合并相近程度最高的两簇成一个新簇,不断重复此过程直到所有个体都归为一个簇[17]。主成分分析法的主要目的是数据降维,通过将原始特征空间进行变换,使少数几个新变量是原变量的线性组合,同时尽可能多地表达原变量的数据特征,在不丢失信息的情况下,消除变量之间可能存在的多重共线性,最终重新生成一个维数更低、各维之间互相独立的特征空间[18]。根据纸张灰烬样品是否含有Cl元素,将样品数据分为两大类,对于每一类样品利用SPSS软件进行统计分析。以含有Cl元素的一类为例,尝试将两种分析方法相结合,以期实现对纸张灰烬的准确分析。

  • 采用组间连接法作为类间亲疏距离的度量方法,平方欧氏距离度量个体距离,进行系统聚类[19],树状聚类图如图 2所示。当并类距离为2时,第1组样品可分为6类,当并类距离为3时,样品可分为5类,当并类距离为6时,样品可分为4类,当并类距离为17时,样品可分为3类,当并类距离为22时,样品可分为两类,阈值达到25时,停止凝聚,所有样品并为一类。为确定分多少类是最科学合理的,需对聚类结果进一步验证。

    Figure 2.  Cluster analysis tree of the first group of samples

  • 采用主成分(principal component, PC)分析,研究变量之间存在线性相关关系,通过降维处理,根据主成分的累计方差贡献率确定主成分个数[20]表 4为主成分累计方差贡献率,确定主成分PC1和PC2,每一个光谱原始数据对应的主成分得分见表 5图 3为主成分得分图。

    principal componentcumulative variance contribution rate/%
    PC193.16
    PC299.86

    Table 4.  Principal component variance cumulative

    sample numberPC1PC2sample numberPC1PC2sample numberPC1PC2
    10.045-0.025130.045-0.005250.045-0.017
    20.045-0.019140.0450.003260.045-0.018
    40.0280.476170.045-0.023270.045-0.021
    60.045-0.020180.0450.061280.045-0.022
    80.045-0.030190.045-0.029290.045-0.007
    90.045-0.025200.0450.000300.045-0.024
    100.0450.012210.045-0.018310.0450.003
    120.045-0.017220.045-0.02432-0.0030.616

    Table 5.  Principal component scores of 24 samples

    Figure 3.  Principal component scores

    图 3可知,据经过主成分分析,可将24个样品大致分为3类,分类结果与系统聚类分析结果一致,两种分析方法得到了相互印证。同理,不含Cl元素的8个纸张灰烬样品经聚类分析和主成分分析后可分为两类。

3.   结论
  • 实验表明,利用X射线荧光光谱法对纸张灰烬样品进行定量与定性分析,根据特征元素的种类和含量可将32个不同来源和用途的纸张灰烬样品进行准确区分;并从多角度对数据进行了多元统计分析,充分挖掘了变量之间的关系,提高了对纸张灰烬样品分析分类的科学性和准确性,可以获得理想的聚类效果。本实验中的方法操作方便,实验结果可靠,且对样品无损,可以准确地通过纸张灰烬样品区分原纸张,可应用于公安实际办案。

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